Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience

Dietary hydrolyzed soya lecithin affects feed intake, abundance of bacteria in the caecum, fatty acid composition and area of adipocytes in pre-mating primiparous V-line female rabbit

This study aimed at investigating the effect of hydrolyzed soya lecithin; also called lysolecithin or lysophosphatidylcholine, on growth performance, caecal microbiota and fat depots in pre-breeding primiparous rabbits does. For this, 60 V-Line primiparous rabbits does (5-6 months) were used in a 30-day experiment. Does were allotted into three iso-nitrogenous iso-caloric dietary treatments (n = 20/group) as follows: (1) CON received 0% soya lecithin, (2) LECL group was fed a basal diet supplemented with 0.5% soya lecithin and (3) LECH group was fed a basal diet supplemented with 1% soya lecithin. Growth performance indices were measured, caecum samples were collected for measurement of specific bacteria via qPCR, and several fat depots including periovarian fat were sampled for adipocyte morphometry and fatty acid profiling. Statistical analysis was performed using GLM procedures of SAS v9.4. Soya lecithin increased feed intake (p < 0.05). The abundance of caecal Bifidobacteria species, Ruminococcus species and phylum Butryvibrio-specific genes increased (p < 0.05) in rabbits receiving soya lecithin in their diet, soya lecithin increased the level of polyunsaturated fatty acids in subcutaneous and perirenal fat (p < 0.05) and increased the level of monounsaturated fatty acids in periovarian fat (p < 0.05); additionally, the adipocyte area increased in periovarian and perirenal fat (p < 0.05). In conclusion, soya lecithin at a dose of 0.5% increased feed intake and energy storage in adipocytes and improved the fatty acid profile of periovarian fat.

A first characterization of the microbiota-resilience link in swine

BACKGROUND

The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.

RESULTS

This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.

CONCLUSION

Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.

Differential patterns in runs of homozygosity in two mice lines under divergent selection for environmental variability for birth weight

Runs of homozygosity (ROH) are defined as long continuous homozygous stretches in the genome which are assumed to originate from a common ancestor. It has been demonstrated that divergent selection for variability in mice is possible and that low variability in birth weight is associated with robustness. To analyse ROH patterns and ROH-based genomic inbreeding, two mouse lines that were divergently selected for birth weight variability for 26 generations were used, with: 752 individuals for the high variability line (H-Line), 766 individuals for the low variability line (L-Line) and 74 individuals as a reference population. Individuals were genotyped using the high density Affymetrix Mouse Diversity Genotyping Array. ROH were identified using both the sliding windows (SW) and the consecutive runs (CR) methods. Inbreeding coefficients were calculated based on pedigree (FPED ) information, on ROH identified using the SW method (FROHSW ) and on ROH identified using the CR method (FROHCR ). Differences in genomic inbreeding were not consistent across generations and these parameters did not show clear differences between lines. Correlations between FPED and FROH were high, particularly for FROHSW . Moreover, correlations between FROHSW and FPED were even higher when ROH were identified with no restrictions in the number of heterozygotes per ROH. The comparison of FROH estimates between either of the selected lines were based on significant differences at the chromosome level, mainly in chromosomes 3, 4, 6, 8, 11, 15 and 19. ROH-based inbreeding estimates that were computed using longer homozygous segments had a higher relationship with FPED . Differences in robustness between lines were not attributable to a higher homozygosis in the L-Line, but maybe to the different distribution of ROH at the chromosome level between lines. The analysis identified a set of genomic regions for future research to establish the genomic basis of robustness.

Selection for environmental variance shifted the gut microbiome composition driving animal resilience

BACKGROUND

Understanding how the host's microbiome shapes phenotypes and participates in the host response to selection is fundamental for evolutionists and animal and plant breeders. Currently, selection for resilience is considered a critical step in improving the sustainability of livestock systems. Environmental variance (V _E), the within-individual variance of a trait, has been successfully used as a proxy for animal resilience. Selection for reduced V _E could effectively shift gut microbiome composition; reshape the inflammatory response, triglyceride, and cholesterol levels; and drive animal resilience. This study aimed to determine the gut microbiome composition underlying the V _E of litter size (LS), for which we performed a metagenomic analysis in two rabbit populations divergently selected for low (n = 36) and high (n = 34) V _E of LS. Partial least square-discriminant analysis and alpha- and beta-diversity were computed to determine the differences in gut microbiome composition among the rabbit populations.

RESULTS

We identified 116 KEGG IDs, 164 COG IDs, and 32 species with differences in abundance between the two rabbit populations studied. These variables achieved a classification performance of the V _E rabbit populations of over than 80%. Compared to the high V _E population, the low V _E (resilient) population was characterized by an underrepresentation of Megasphaera sp., Acetatifactor muris, Bacteroidetes rodentium, Ruminococcus bromii, Bacteroidetes togonis, and Eggerthella sp. and greater abundances of Alistipes shahii, Alistipes putredinis, Odoribacter splanchnicus, Limosilactobacillus fermentum, and Sutterella, among others. Differences in abundance were also found in pathways related to biofilm formation, quorum sensing, glutamate, and amino acid aromatic metabolism. All these results suggest differences in gut immunity modulation, closely related to resilience.

CONCLUSIONS

This is the first study to show that selection for V _E of LS can shift the gut microbiome composition. The results revealed differences in microbiome composition related to gut immunity modulation, which could contribute to the differences in resilience among rabbit populations. The selection-driven shifts in gut microbiome composition should make a substantial contribution to the remarkable genetic response observed in the V _E rabbit populations. Video Abstract.

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

BACKGROUND

Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F_ST, and I_n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities.

RESULTS

We found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.

CONCLUSIONS

The current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.

Effect of environmental variance-based resilience selection on the gut metabolome of rabbits

BACKGROUND

Gut metabolites are key actors in host-microbiota crosstalk with effect on health. The study of the gut metabolome is an emerging topic in livestock, which can help understand its effect on key traits such as animal resilience and welfare. Animal resilience has now become a major trait of interest because of the high demand for more sustainable production. Composition of the gut microbiome can reveal mechanisms that underlie animal resilience because of its influence on host immunity. Environmental variance (V_E), specifically the residual variance, is one measure of resilience. The aim of this study was to identify gut metabolites that underlie differences in the resilience potential of animals originating from a divergent selection for V_E of litter size (LS). We performed an untargeted gut metabolome analysis in two divergent rabbit populations for low (n = 13) and high (n = 13) V_E of LS. Partial least square-discriminant analysis was undertaken, and Bayesian statistics were computed to determine dissimilarities in the gut metabolites between these two rabbit populations.

RESULTS

We identified 15 metabolites that discriminate rabbits from the divergent populations with a prediction performance of 99.2% and 90.4% for the resilient and non-resilient populations, respectively. These metabolites were suggested to be biomarkers of animal resilience as they were the most reliable. Among these, five that derived from the microbiota metabolism (3-(4-hydroxyphenyl)lactate, 5-aminovalerate, and equol, N6-acetyllysine, and serine), were suggested to be indicators of dissimilarities in the microbiome composition between the rabbit populations. The abundances of acylcarnitines and metabolites derived from the phenylalanine, tyrosine, and tryptophan metabolism were low in the resilient population and these pathways can, therefore impact the inflammatory response and health status of animals.

CONCLUSIONS

This is the first study to identify gut metabolites that could act as potential resilience biomarkers. The results support differences in resilience between the two studied rabbit populations that were generated by selection for V_E of LS. Furthermore, selection for V_E of LS modified the gut metabolome, which could be another factor that modulates animal resilience. Further studies are needed to determine the causal role of these metabolites in health and disease.

Comparative analysis of genomic inbreeding parameters and runs of homozygosity islands in several fancy and meat rabbit breeds

Runs of homozygosity (ROH) are defined as long stretches of DNA homozygous at each polymorphic position. The proportion of genome covered by ROH and their length are indicators of the level and origin of inbreeding. In this study, we analysed SNP chip datasets (obtained using the Axiom OrcunSNP Array) of a total of 702 rabbits from 12 fancy breeds and four meat breeds to identify ROH with different approaches and calculate several genomic inbreeding parameters. The highest average number of ROH per animal was detected in Belgian Hare (~150) and the lowest in Italian Silver (~106). The average length of ROH ranged from 4.001 ± 0.556 Mb in Italian White to 6.268 ± 1.355 Mb in Ermine. The same two breeds had the lowest (427.9 ± 86.4 Mb, Italian White) and the highest (921.3 ± 179.8 Mb, Ermine) average values of the sum of all ROH segments. More fancy breeds had a higher level of genomic inbreeding (as defined by ROH) than meat breeds. Several ROH islands contain genes involved in body size, body length, pigmentation processes, carcass traits, growth, and reproduction traits (e.g.: AOX1, GPX5, IFRD1, ITGB8, NELL1, NR3C1, OCA2, TRIB1, TRIB2). Genomic inbreeding parameters can be useful to overcome the lack of information in the management of rabbit genetic resources. ROH provided information to understand, to some extent, the genetic history of rabbit breeds and to identify signatures of selection in the rabbit genome.